模拟化学反应系统的快速无偏T-Leap算法

化学反应系统中的Leap算法可在获得较好精度的同时大幅提高模拟速度.最近提出的无偏Leap方法有效地克服了由Leap时间区间内的反应次数的近似均值与真实均值之间的偏差引起的Leap算法的误差的不足.本文讨论了一个基于物种相对改变估计真实均值的快速无偏T-Leap算法,并将该算法推广到模拟时滞化学系统中.该快速算法具有易于编码、比前者更快等优点.当系统中的反应通道或物种的数目较大时,该方法具有更明显的速度优势.     

模拟时滞化学反应系统的自适应τ-Leap算法

本文发展了一种模拟时滞化学反应系统的自适应τ-Leap算法（DAr—Leap）．该算法将后验τ-Leap算法应用到时滞化学反应系统，能够自动调节τ使得在时间区间[t，t＋τ）内发生多次反应事件并且精确地满足Leap条件，从而避免了负分子数目的产生，较大地提高了模拟速度．     

任意秩有限总体中无偏预测和估计的构造关系

研究了有限总体均值向量的无偏估计和线性可预测变量的无偏预测之间的关系,利用分块矩阵广义逆直接对加权风险函数进行分解,提出了一种由均值向量的无偏估计来构造无偏预测的新方法,并找到了它们之间的构造关系.特别地,线性可预测变量的最优线性无偏预测(BLUP)可由均值向量的最佳线性无偏估计(BLUE)惟一地表示(有关惟一性在几乎处处意义下理解).     

集团抽样不等概方法的均值估计及其性质

在集团抽样调查中，[1]和[2]给出了一般抽样方法及均值估计和它性质的论述，但未考虑各集团在总体中的地位对估计结果的影响，本文给出了集团抽样的不等概方法和均值的无偏估计，同时也给出了这种估计的方差及方差的无偏估计。     

基于样本广义方差的改进√|S|控制图

以常用的广义方差为基础,利用矩估计的方法,从一组样本推广到多组独立样本,得出具有无偏控制限的改进√|S|控制图,并对比说明了矩估计方法的简洁性和准确性.与标准√|S|控制图相比,其具有更小的平均运行链长(ARL).结合实例,采用数值模拟的方法说明该控制图对过程中方差的漂移有较强的检测能力.     

随机模拟化学反应系统的加速L-leap算法

提出了随机模拟化学反应系统的加速L-leap算法,该算法根据leap条件确定具有最大倾向函数的反应通道的反应次数,并利用二项分布随机数生成其它反应通道在当前leap时间区间内的反应次数．L-leap算法可更好地满足leap条件．数值模拟实验表明该算法能取得更好的模拟性能．     

基于低记录值的Ⅰ型GLD的统计推断

记录值在天气、经济和体育数据中有重要的应用。本文研究了GLD的低记录值的矩、方差和协方差,构成均值向量α和协方差阵Σ;并由此对GLD的位置参数和尺度参数进行最佳线性无偏估计;利用广义线性模型得到GLD的低记录值最佳线性无偏预测;利用蒙特卡洛模拟显示:样本量不同且形状参数b取不同值时,低记录值的参数估计和预测效果较好,其偏差和均方误差都较小,说明此方法实用且有效。     

排序集抽样下指数分布参数的最优线性无偏估计

排序集抽样方法适用于样本测量困难但排序容易的场合,其样本包含了次序信息.指数分布在寿命试验中占有非常重要的地位,为了提高指数分布参数的估计效率,本文提出了排序集抽样下参数的最优线性无偏估计量,计算了新估计量的方差,证明了其具有渐近正态性.相对效率和模拟效率的研究结果表明:新估计量的估计效率不仅高于简单随机抽样下一致最小方差无偏估计量,也高于排序集抽样下样本均值和修正极大似然估计量.最后,将推荐方法应用到转移性肾癌的临床研究中,从而验证该方法的有效性.     

k-均值算法的初始化方法综述

k-均值问题自提出以来一直吸引组合优化和计算机科学领域的广泛关注, 是经典的NP-难问题之一. 给定N个d维实向量构成的观测集, 目标是把这N个观测点划分到k(\leq N)个集合中, 使得所有集合中的点到对应的聚类中心距离的平方和最小, 一个集合的聚类中心指的是该集合 中所有观测点的均值. k-均值算法作为解决k-均值问题的启发式算法,在实际应用中因其出色的收敛速度而倍受欢迎. k-均值算法可描述为: 给定问题的初始化分组, 交替进行指派(将观测点分配到离其最近的均值点)和更新(计算新的聚类的均值点)直到收敛到某一解. 该算法通常被认为几乎是线性收敛的. 但缺点也很明显, 无法保证得到的是全局最优解, 并且算法结果好坏过于依赖初始解的选取. 于是学者们纷纷提出不同的初始化方法来提高k-均值算法的质量. 现筛选和罗列了关于选取初始解的k-均值算法的初始化方法供读者参考.     

基于混合单纯形算法的模糊均值图像分割

针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.     

Mixing Model Based on Parameterized Scalar Profiles

The composition fields in turbulent reacting flows are affected by turbulent transport (macromixing), molecular diffusion (micromixing), and chemical reactions. In the joint velocity-composition probability density function transport equation the highly nonlinear macromixing and chemical reaction terms appear in closed form. This is a considerable advantage over second moment closure methods. Micromixing on the other hand requires modelling and especially for turbulent combustion accurate mixing models are crucial. In this paper we present an approach to model the mixing of scalars, e.g. species mass fractions or temperature, based on considering one-dimensional parameterized scalar profiles (PSP). (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Parameterized Scalar Profile Mixing Model for Turbulent Combustion Simulations

The composition fields in turbulent reacting flows are affected by turbulent transport (macromixing), molecular diffusion (micromixing), and chemical reactions. In the joint velocity-composition probability density function transport equation the highly non-linear macromixing and chemical reaction terms appear in closed form. This is a considerable advantage over moment closure methods. Micromixing on the other hand requires modeling and especially for turbulent combustion accurate mixing models are crucial. Our approach to model the mixing of scalars, e.g. species mass fractions or temperature, is based on considering one-dimensional parameterized scalar profiles (PSP). Here, an extension of the PSP mixing model to inhomogeneous flows is presented. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Adaptive,Delayed-Acceptance MCMC for Targets With Expensive Likelihoods

When conducting Bayesian inference, delayed-acceptance (DA) Metropolis–Hastings (MH) algorithms and DA pseudo-marginal MH algorithms can be applied when it is computationally expensive to calculate the true posterior or an unbiased estimate thereof, but a computationally cheap approximation is available. A first accept-reject stage is applied, with the cheap approximation substituted for the true posterior in the MH acceptance ratio. Only for those proposals that pass through the first stage is the computationally expensive true posterior (or unbiased estimate thereof) evaluated, with a second accept-reject stage ensuring that detailed balance is satisfied with respect to the intended true posterior. In some scenarios, there is no obvious computationally cheap approximation. A weighted average of previous evaluations of the computationally expensive posterior provides a generic approximation to the posterior. If only the k-nearest neighbors have nonzero weights then evaluation of the approximate posterior can be made computationally cheap provided that the points at which the posterior has been evaluated are stored in a multi-dimensional binary tree, known as a KD-tree. The contents of the KD-tree are potentially updated after every computationally intensive evaluation. The resulting adaptive, delayed-acceptance [pseudo-marginal] Metropolis–Hastings algorithm is justified both theoretically and empirically. Guidance on tuning parameters is provided and the methodology is applied to a discretely observed Markov jump process characterizing predator–prey interactions and an ODE system describing the dynamics of an autoregulatory gene network. Supplementary material for this article is available online.     

Instantaneous limits of reversible chemical reactions in presence of macroscopic convection

Chemically reacting systems frequently involve fast reversible reactions, additional slow reactions as well as mass transport due to macroscopic convection. In this situation, the passage to infinite reaction speed is a means to reduce the complexity of the reaction kinetics and to avoid the need for explicit values of the rate constants. Thereby the large stiffness of the original system of differential equations is also removed.In the present paper this instantaneous reaction limit is studied for systems with independent fast reversible reactions, where the rate functions are given by mass-action kinetics. Under realistic assumptions the limiting dynamical system is derived and convergence of the solutions is obtained as the rate constants tend to infinity. The proof is based on Lyapunov functions techniques and exploits the structure of rate functions that results from mass-action kinetics.This approach is complementary to the quasi-steady-state approximation which is often applied in chemical engineering. The differences are illustrated by means of a classical enzyme-substrate reaction scheme.     

Intermittent demand: Linking forecasting to inventory obsolescence

The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand size and demand interval whenever a positive demand occurs, and their ratio provides the forecast of demand per period. The Croston method has two important disadvantages. First and foremost, not updating after (many) periods with zero demand renders the method unsuitable for dealing with obsolescence issues. Second, the method is positively biased and this is true for all points in time (i.e. considering the forecasts made at an arbitrary time period) and issue points only (i.e. considering the forecasts following a positive demand occurrence only). The second issue has been addressed in the literature by the proposal of an estimator (Syntetos-Boylan Approximation, SBA) that is approximately unbiased. In this paper, we propose a new method that overcomes both these shortcomings while not adding complexity. Different from the Croston method, the new method is unbiased (for all points in time) and it updates the demand probability instead of the demand interval, doing so in every period. The comparative merits of the new estimator are assessed by means of an extensive simulation experiment. The results indicate its superior performance and enable insights to be gained into the linkage between demand forecasting and obsolescence.     

On a class of almost unbiased ratio estimators

Summary Murthy and Nanjamma [4] studied the problem of construction of almost unbiased ratio estimators for any sampling design using the technique of interpenetrating subsamples. Subsequently, Rao [7], [8] has given a general method of constructing unbiased ratio estimators by considering linear combinations of the two simple estimators based on the ratio of means and the mean of ratios. However, it is difficult to choose an optimum weight (Rao [9]) which minimizes the variance of the combined estimator since the weights are random in certain cases. In this note, we consider a different method of combining these estimators and obtain a general class of almost unbiased ratio estimators of which Murthy and Nanjamma's is a particular case and derive an optimum in this class. The case of simple random sampling where a similar class of almost unbiased ratio estimators can be developed is briefly discussed. The results are illustrated by means of simple numerical examples.     

电力系统中地网腐蚀诊断的一种新的数学模型及其仿真计算

对电力系统中具有重大应用价值的地网腐蚀诊断问题抽象出仿真求解的一种新的数学模型:即求解带约束的非线性隐式方程组模型.但由于问题本身的物理特性决定了所建立的数学模型具有以下特点:一是非线性方程组为欠定方程组,而且非线性程度非常高;二是方程组的所有函数均为隐函数;三是方程组附加若干箱约束条件.这种特性给模型分析与算法设计带来巨大困难.对于欠定方程组的求解,文中根据工程实际背景,尽可能地扩充方程的个数,使之成为超定方程组,然后对欠定方程组和超定方程组分别求解并进行比较.将带约束的非线性隐函数方程组求解问题,转化为无约束非线性最小二乘问题,并采用矩阵求导等技术和各种算法设计技巧克服隐函数的计算困难,最后使用拟牛顿信赖域方法进行计算.大量的计算实例表明,文中所提出的数学模型及求解方法是可行的.与目前广泛采用的工程简化模型相比较,在模型和算法上具有很大优势.     

Reliability and covariance estimation of weighted k-out-of-n multi-state systems

In the literature of reliability engineering, reliability of the weighted k-out-of-n system can be calculated using component reliability based on the structure function. The calculation usually assumes that the true component reliability is completely known. However, this is not the case in practical applications. Instead, component reliability has to be estimated using empirical sample data. Uncertainty arises during this estimation process and propagates to the system level. This paper studies the propagation mechanism of estimation uncertainty through the universal generating function method. Equations of the complete solution including the unbiased system reliability estimator and the corresponding unbiased covariance estimator are derived. This is a unified approach. It can be applied to weighted k-out-of-n systems with multi-state components, to weighted k-out-of-n systems with binary components, and to simple series and parallel systems. It may also serve as building blocks to derive estimators of system reliability and uncertainty measures for more complicated systems.     

Discretization of the Navier‐Stokes equations with slip boundary condition

We propose and analyze a two‐level method of discretizing the nonlinear Navier‐Stokes equations with slip boundary condition. The slip boundary condition is appropriate for problems that involve free boundaries, flows past chemically reacting walls, and other examples where the usual no‐slip condition u = 0 is not valid. The two‐level algorithm consists of solving a small nonlinear system of equations on the coarse mesh and then using that solution to solve a larger linear system on the fine mesh. The two‐level method exploits the quadratic nonlinearity in the Navier‐Stokes equations. Our error estimates show that it has optimal order accuracy, provided that the best approximation to the true solution in the velocity and pressure spaces is bounded above by the data. © 2001 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 17: 26–42, 2001